Overview

Dataset statistics

Number of variables19
Number of observations4254339
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 GiB
Average record size in memory895.0 B

Variable types

Numeric6
DateTime1
Categorical12

Alerts

store_name has a high cardinality: 2257 distinct values High cardinality
deposit/distributor has a high cardinality: 77 distinct values High cardinality
municipality/city has a high cardinality: 606 distinct values High cardinality
units is highly correlated with totalHigh correlation
total is highly correlated with unitsHigh correlation
units is highly correlated with totalHigh correlation
total is highly correlated with unitsHigh correlation
units is highly correlated with totalHigh correlation
total is highly correlated with unitsHigh correlation
division is highly correlated with grade and 3 other fieldsHigh correlation
sku_subcategory is highly correlated with sku_description and 1 other fieldsHigh correlation
format is highly correlated with supplierHigh correlation
grade is highly correlated with division and 3 other fieldsHigh correlation
supplier is highly correlated with format and 1 other fieldsHigh correlation
sku_description is highly correlated with sku_subcategory and 2 other fieldsHigh correlation
state is highly correlated with division and 3 other fieldsHigh correlation
structure is highly correlated with division and 3 other fieldsHigh correlation
sku_category is highly correlated with sku_subcategory and 1 other fieldsHigh correlation
deposit/distributor is highly correlated with division and 3 other fieldsHigh correlation
id is highly correlated with store_id and 3 other fieldsHigh correlation
store_id is highly correlated with id and 3 other fieldsHigh correlation
format is highly correlated with id and 6 other fieldsHigh correlation
structure is highly correlated with format and 4 other fieldsHigh correlation
division is highly correlated with structure and 3 other fieldsHigh correlation
deposit/distributor is highly correlated with store_id and 5 other fieldsHigh correlation
state is highly correlated with format and 4 other fieldsHigh correlation
supplier is highly correlated with id and 3 other fieldsHigh correlation
grade is highly correlated with structure and 3 other fieldsHigh correlation
sku is highly correlated with sku_description and 3 other fieldsHigh correlation
sku_description is highly correlated with id and 7 other fieldsHigh correlation
sku_category is highly correlated with sku and 3 other fieldsHigh correlation
sku_subcategory is highly correlated with sku and 3 other fieldsHigh correlation
units is highly correlated with unit_price and 1 other fieldsHigh correlation
unit_price is highly correlated with sku and 5 other fieldsHigh correlation
total is highly correlated with sku_description and 2 other fieldsHigh correlation
id has unique values Unique

Reproduction

Analysis started2021-10-17 15:56:36.295369
Analysis finished2021-10-17 16:00:51.770806
Duration4 minutes and 15.48 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4254339
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2638617.371
Minimum0
Maximum5489595
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size48.7 MiB
2021-10-17T18:00:51.856805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile259874.9
Q11310655.5
median2627263
Q33950203
95-th percentile5081388.4
Maximum5489595
Range5489595
Interquartile range (IQR)2639547.5

Descriptive statistics

Standard deviation1537052.559
Coefficient of variation (CV)0.5825219585
Kurtosis-1.164308021
Mean2638617.371
Median Absolute Deviation (MAD)1319790
Skewness0.03271820983
Sum1.122557279 × 1013
Variance2.362530568 × 1012
MonotonicityNot monotonic
2021-10-17T18:00:51.961806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41963511
 
< 0.1%
46770551
 
< 0.1%
3742421
 
< 0.1%
3701441
 
< 0.1%
44804791
 
< 0.1%
2841261
 
< 0.1%
44886671
 
< 0.1%
2923141
 
< 0.1%
44845691
 
< 0.1%
44640871
 
< 0.1%
Other values (4254329)4254329
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
54895951
< 0.1%
54895871
< 0.1%
54895841
< 0.1%
54895801
< 0.1%
54895771
< 0.1%
54895731
< 0.1%
54895711
< 0.1%
54895701
< 0.1%
54895641
< 0.1%
54895571
< 0.1%

date
Date

Distinct397
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size64.9 MiB
Minimum2016-01-01 00:00:00
Maximum2017-06-04 00:00:00
2021-10-17T18:00:52.101804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:52.210804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

store_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2311
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2636.168118
Minimum2
Maximum5855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.9 MiB
2021-10-17T18:00:52.341806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile239
Q11661
median2676
Q33703
95-th percentile5440
Maximum5855
Range5853
Interquartile range (IQR)2042

Descriptive statistics

Standard deviation1330.483814
Coefficient of variation (CV)0.504703704
Kurtosis-0.257816153
Mean2636.168118
Median Absolute Deviation (MAD)1020
Skewness0.1858588328
Sum1.121515284 × 1010
Variance1770187.178
MonotonicityNot monotonic
2021-10-17T18:00:52.455804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10034460
 
0.1%
10164201
 
0.1%
37474195
 
0.1%
17704152
 
0.1%
23844128
 
0.1%
10844116
 
0.1%
35054109
 
0.1%
38954055
 
0.1%
40734045
 
0.1%
38474044
 
0.1%
Other values (2301)4212834
99.0%
ValueCountFrequency (%)
21800
< 0.1%
41111
< 0.1%
51585
< 0.1%
61092
< 0.1%
71149
< 0.1%
81099
< 0.1%
91381
< 0.1%
101287
< 0.1%
111064
< 0.1%
121566
< 0.1%
ValueCountFrequency (%)
58553433
0.1%
58512866
0.1%
58502889
0.1%
58442619
0.1%
58433221
0.1%
58273211
0.1%
58253646
0.1%
58173387
0.1%
58153544
0.1%
58132701
0.1%

format
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size295.9 MiB
Grocery
1202193 
AllInOne
1147815 
Leftorium
814123 
SmallShop
430472 
GipsyTrade
315696 
Other values (4)
344040 

Length

Max length10
Median length8
Mean length7.925143248
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrocery
2nd rowAllInOne
3rd rowAllInOne
4th rowLeftorium
5th rowGrocery

Common Values

ValueCountFrequency (%)
Grocery1202193
28.3%
AllInOne1147815
27.0%
Leftorium814123
19.1%
SmallShop430472
 
10.1%
GipsyTrade315696
 
7.4%
Shop263019
 
6.2%
GreatShop68538
 
1.6%
TinyShop8122
 
0.2%
BigCom4361
 
0.1%

Length

2021-10-17T18:00:52.578806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-17T18:00:52.664806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
grocery1202193
28.3%
allinone1147815
27.0%
leftorium814123
19.1%
smallshop430472
 
10.1%
gipsytrade315696
 
7.4%
shop263019
 
6.2%
greatshop68538
 
1.6%
tinyshop8122
 
0.2%
bigcom4361
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

store_name
Categorical

HIGH CARDINALITY

Distinct2257
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size349.7 MiB
Bizarre Crimson Fishmonger
 
6596
Bloody Chocolate AllInOne
 
6305
Enlightened Cyan HairDresser
 
5854
Enlightened Lavender GipsyTrade
 
5755
Bad Sky Blue Mall
 
5580
Other values (2252)
4224249 

Length

Max length33
Median length21
Mean length21.19869127
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOld Indigo BigCom
2nd rowDead Burgundy Shop
3rd rowKicking Orange HairDresser
4th rowFunny Maroon LunchHall
5th rowDefiant Red Mall

Common Values

ValueCountFrequency (%)
Bizarre Crimson Fishmonger6596
 
0.2%
Bloody Chocolate AllInOne6305
 
0.1%
Enlightened Cyan HairDresser5854
 
0.1%
Enlightened Lavender GipsyTrade5755
 
0.1%
Bad Sky Blue Mall5580
 
0.1%
Defiant Orange Butcher5480
 
0.1%
Awesome Lemon BookShop5416
 
0.1%
Kicking Brown Convenience5294
 
0.1%
Short Olive BigCom5291
 
0.1%
Horrible Black Leftorium5086
 
0.1%
Other values (2247)4197682
98.7%

Length

2021-10-17T18:00:53.118819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
green233923
 
1.8%
blue225298
 
1.7%
shop158409
 
1.2%
stationer156755
 
1.2%
fishmonger153940
 
1.2%
market153812
 
1.2%
butcher149862
 
1.2%
deli148909
 
1.2%
grocery147076
 
1.1%
baker145027
 
1.1%
Other values (102)11232126
87.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

structure
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size294.7 MiB
FORANEO
2013950 
METRO
1441487 
DISTRIBUIDORES
798902 

Length

Max length14
Median length7
Mean length7.636841587
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFORANEO
2nd rowDISTRIBUIDORES
3rd rowFORANEO
4th rowFORANEO
5th rowMETRO

Common Values

ValueCountFrequency (%)
FORANEO2013950
47.3%
METRO1441487
33.9%
DISTRIBUIDORES798902
 
18.8%

Length

2021-10-17T18:00:53.224806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-17T18:00:53.285806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
foraneo2013950
47.3%
metro1441487
33.9%
distribuidores798902
 
18.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

division
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size318.7 MiB
METRO-NORTE
773808 
METRO-SUR
667679 
CENTRO
531307 
BAJÍO
481556 
OCCIDENTE
407709 
Other values (6)
1392280 

Length

Max length11
Median length9
Mean length8.216916894
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCENTRO
2nd rowCENTRO
3rd rowCENTRO-SUR
4th rowCENTRO
5th rowMETRO-NORTE

Common Values

ValueCountFrequency (%)
METRO-NORTE773808
18.2%
METRO-SUR667679
15.7%
CENTRO531307
12.5%
BAJÍO481556
11.3%
OCCIDENTE407709
9.6%
SURESTE362945
8.5%
NORESTE276112
 
6.5%
CENTRO-SUR225405
 
5.3%
PENÍNSULA221555
 
5.2%
NOROESTE183196
 
4.3%

Length

2021-10-17T18:00:53.370805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metro-norte773808
18.2%
metro-sur667679
15.7%
centro531307
12.5%
bajío481556
11.3%
occidente407709
9.6%
sureste362945
8.5%
noreste276112
 
6.5%
centro-sur225405
 
5.3%
península221555
 
5.2%
noroeste183196
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

deposit/distributor
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.6 MiB
Emolor Vocals
347313 
Colorvit Ifonforge Industries
 
202871
Lorthogus Toughworks
 
198650
Thrilthoal Empire
 
182213
Thrilmamad Vocals
 
162288
Other values (72)
3161004 

Length

Max length32
Median length17
Mean length18.2180059
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVitena Vocals
2nd rowMovitalf Empire
3rd rowGorngusco Toughworks
4th rowElortho Aerospace
5th rowMarvitrus Ifonforge Industries

Common Values

ValueCountFrequency (%)
Emolor Vocals347313
 
8.2%
Colorvit Ifonforge Industries202871
 
4.8%
Lorthogus Toughworks198650
 
4.7%
Thrilthoal Empire182213
 
4.3%
Thrilmamad Vocals162288
 
3.8%
Thrilvitar Industrail153234
 
3.6%
Ioalfio Ifonforge Industries149574
 
3.5%
Thrilvallor Corp.135984
 
3.2%
Elortho Aerospace128190
 
3.0%
Barrusgul Corp.113824
 
2.7%
Other values (67)2480198
58.3%

Length

2021-10-17T18:00:53.477807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vocals1017026
 
11.1%
ifonforge665559
 
7.3%
industries665559
 
7.3%
corp546994
 
6.0%
industrail518874
 
5.7%
toughworks427009
 
4.7%
emolor347313
 
3.8%
empire344638
 
3.8%
inc307644
 
3.4%
aerospace291461
 
3.2%
Other values (76)4042160
44.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

state
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size296.8 MiB
Alloralf
958488 
Magornmar
675352 
Thrilvallor
230976 
Coalmar
225968 
Barthovit
222572 
Other values (27)
1940983 

Length

Max length11
Median length8
Mean length8.146733723
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThrilvallor
2nd rowAlloralf
3rd rowLorgulgus
4th rowAlloralf
5th rowMagornmar

Common Values

ValueCountFrequency (%)
Alloralf958488
22.5%
Magornmar675352
15.9%
Thrilvallor230976
 
5.4%
Coalmar225968
 
5.3%
Barthovit222572
 
5.2%
Erodmo201881
 
4.7%
Nabargus198445
 
4.7%
Alfrusma156322
 
3.7%
Lorvalmo127892
 
3.0%
Lorgulgus118347
 
2.8%
Other values (22)1138096
26.8%

Length

2021-10-17T18:00:53.588806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alloralf958488
22.5%
magornmar675352
15.9%
thrilvallor230976
 
5.4%
coalmar225968
 
5.3%
barthovit222572
 
5.2%
erodmo201881
 
4.7%
nabargus198445
 
4.7%
alfrusma156322
 
3.7%
lorvalmo127892
 
3.0%
lorgulgus118347
 
2.8%
Other values (22)1138096
26.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

municipality/city
Categorical

HIGH CARDINALITY

Distinct606
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size341.5 MiB
Enlightened Dark Empire
631506 
Purple Creek
 
143626
Evil Chocolate Rock
 
107966
Empty Chartreuse Butter
 
90343
First Salmon Rock
 
81053
Other values (601)
3199845 

Length

Max length29
Median length19
Mean length19.17095887
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row Purple Creek
2nd rowLong Chocolate Barrier
3rd rowHorrible Purple Tree
4th rowFunny Magenta Tree
5th rowEnlightened Dark Empire

Common Values

ValueCountFrequency (%)
Enlightened Dark Empire631506
 
14.8%
Purple Creek143626
 
3.4%
Evil Chocolate Rock107966
 
2.5%
Empty Chartreuse Butter90343
 
2.1%
First Salmon Rock81053
 
1.9%
Evil Azure Metropolis71587
 
1.7%
Long Lavender Butter70285
 
1.7%
Last Pink River69073
 
1.6%
Singing Grey Empire66939
 
1.6%
Little Silver Polis66320
 
1.6%
Other values (596)2855641
67.1%

Length

2021-10-17T18:00:53.704806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
empire792838
 
6.3%
dark691450
 
5.5%
enlightened683419
 
5.4%
chocolate305671
 
2.4%
rock292359
 
2.3%
evil285560
 
2.3%
purple236317
 
1.9%
chartreuse232293
 
1.8%
rare206077
 
1.6%
butter200182
 
1.6%
Other values (108)8684380
68.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

supplier
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size308.4 MiB
proveedor_2
3922782 
proveedor_1
 
331557

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowproveedor_2
2nd rowproveedor_2
3rd rowproveedor_2
4th rowproveedor_2
5th rowproveedor_2

Common Values

ValueCountFrequency (%)
proveedor_23922782
92.2%
proveedor_1331557
 
7.8%

Length

2021-10-17T18:00:53.823806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-17T18:00:53.897803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
proveedor_23922782
92.2%
proveedor_1331557
 
7.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

grade
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size271.7 MiB
IV
1644414 
V
675352 
VI
659255 
III
647166 
II
529689 

Length

Max length3
Median length2
Mean length1.970230628
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIV
2nd rowIV
3rd rowIV
4th rowIV
5th rowV

Common Values

ValueCountFrequency (%)
IV1644414
38.7%
V675352
15.9%
VI659255
15.5%
III647166
 
15.2%
II529689
 
12.5%
I98463
 
2.3%

Length

2021-10-17T18:00:53.982805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-17T18:00:54.062812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
iv1644414
38.7%
v675352
15.9%
vi659255
15.5%
iii647166
 
15.2%
ii529689
 
12.5%
i98463
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sku
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4455742.654
Minimum4220015
Maximum4829827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.9 MiB
2021-10-17T18:00:54.176804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4220015
5-th percentile4240039
Q14290336
median4442378
Q34615034
95-th percentile4665676
Maximum4829827
Range609812
Interquartile range (IQR)324698

Descriptive statistics

Standard deviation155313.5231
Coefficient of variation (CV)0.03485693297
Kurtosis-1.330780289
Mean4455742.654
Median Absolute Deviation (MAD)152042
Skewness0.08813030507
Sum1.895623975 × 1013
Variance2.412229044 × 1010
MonotonicityNot monotonic
2021-10-17T18:00:54.290803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
4554143289770
 
6.8%
4270121288587
 
6.8%
4240039285323
 
6.7%
4330800276087
 
6.5%
4381425273462
 
6.4%
4290336260236
 
6.1%
4442378254123
 
6.0%
4250046252974
 
5.9%
4412040238867
 
5.6%
4533931207049
 
4.9%
Other values (36)1627861
38.3%
ValueCountFrequency (%)
42200157100
 
0.2%
42300229114
 
0.2%
4240039285323
6.7%
4250046252974
5.9%
426005332516
 
0.8%
4270121288587
6.8%
4290336260236
6.1%
43003433
 
< 0.1%
4330800276087
6.5%
43510438059
 
0.2%
ValueCountFrequency (%)
48298277209
 
0.2%
48198109482
 
0.2%
48097978790
 
0.2%
478954464
 
< 0.1%
477953732
 
< 0.1%
476952036
 
< 0.1%
47594384
 
< 0.1%
474942121
 
< 0.1%
473941414
 
< 0.1%
472919329589
0.7%

sku_description
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size346.8 MiB
Stars Mango Peach
289770 
Squares Chocolate
288587 
Lucjy Charms Chocolate
285323 
Crunch Mango Peach
 
276087
Chocos Mango Peach
 
273462
Other values (41)
2841110 

Length

Max length42
Median length18
Mean length20.47953348
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCups Mango Peach
2nd rowCups Mango Peach
3rd rowCups Mango Peach
4th rowCups Mango Peach
5th rowCups Mango Peach

Common Values

ValueCountFrequency (%)
Stars Mango Peach289770
 
6.8%
Squares Chocolate288587
 
6.8%
Lucjy Charms Chocolate285323
 
6.7%
Crunch Mango Peach276087
 
6.5%
Chocos Mango Peach273462
 
6.4%
Toast Chocolate260236
 
6.1%
Crunchy Bran Mango Peach254123
 
6.0%
Muesli Chocolate252974
 
5.9%
Crispy Rice Mango Peach238867
 
5.6%
Cups Mango Peach207049
 
4.9%
Other values (36)1627861
38.3%

Length

2021-10-17T18:00:54.412804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mango2408661
17.9%
peach2408661
17.9%
chocolate1845678
13.7%
choc709828
 
5.3%
chip709828
 
5.3%
crunch510234
 
3.8%
toast450667
 
3.4%
squares416108
 
3.1%
stars296870
 
2.2%
chocos295026
 
2.2%
Other values (31)3373946
25.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sku_category
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size308.6 MiB
Sugar-free
2741950 
Super-flavour
1512389 

Length

Max length13
Median length10
Mean length11.06647989
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuper-flavour
2nd rowSuper-flavour
3rd rowSuper-flavour
4th rowSuper-flavour
5th rowSuper-flavour

Common Values

ValueCountFrequency (%)
Sugar-free2741950
64.5%
Super-flavour1512389
35.5%

Length

2021-10-17T18:00:54.512804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-17T18:00:54.583804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
sugar-free2741950
64.5%
super-flavour1512389
35.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

sku_subcategory
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.9 MiB
Cereal bars
2741950 
Cereal box
1512389 

Length

Max length11
Median length11
Mean length10.6445067
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCereal box
2nd rowCereal box
3rd rowCereal box
4th rowCereal box
5th rowCereal box

Common Values

ValueCountFrequency (%)
Cereal bars2741950
64.5%
Cereal box1512389
35.5%

Length

2021-10-17T18:00:54.660805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-17T18:00:54.760805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
cereal4254339
50.0%
bars2741950
32.2%
box1512389
 
17.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

units
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct352
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.471704587
Minimum0.2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.9 MiB
2021-10-17T18:00:54.844816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.25
Q11
median3
Q39
95-th percentile25
Maximum36
Range35.8
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.975320511
Coefficient of variation (CV)1.232336922
Kurtosis2.32100284
Mean6.471704587
Median Absolute Deviation (MAD)2.4
Skewness1.726872008
Sum27532825.22
Variance63.60573726
MonotonicityNot monotonic
2021-10-17T18:00:54.947809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1373963
 
8.8%
2280259
 
6.6%
3202443
 
4.8%
0.2169183
 
4.0%
4164160
 
3.9%
0.45134102
 
3.2%
5130206
 
3.1%
6115734
 
2.7%
0.25112142
 
2.6%
0.4108804
 
2.6%
Other values (342)2463343
57.9%
ValueCountFrequency (%)
0.2169183
4.0%
0.25112142
 
2.6%
0.4108804
 
2.6%
0.45134102
 
3.2%
0.593402
 
2.2%
0.670646
 
1.7%
0.7564999
 
1.5%
0.848900
 
1.1%
0.987473
 
2.1%
1373963
8.8%
ValueCountFrequency (%)
3610291
0.2%
35.91135
 
< 0.1%
35.814
 
< 0.1%
35.7589
 
< 0.1%
35.67
 
< 0.1%
35.5527
 
< 0.1%
35.565
 
< 0.1%
35.413
 
< 0.1%
35.2586
 
< 0.1%
35.215
 
< 0.1%

unit_price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.48734904
Minimum1
Maximum28.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.9 MiB
2021-10-17T18:00:55.058809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.17
Q110.1
median16
Q317.6
95-th percentile22.6
Maximum28.2
Range27.2
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.193937571
Coefficient of variation (CV)0.3585153887
Kurtosis-0.729426986
Mean14.48734904
Median Absolute Deviation (MAD)3.6
Skewness-0.1557830016
Sum61634094.02
Variance26.97698749
MonotonicityNot monotonic
2021-10-17T18:00:55.184804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16255916
 
6.0%
9.7242760
 
5.7%
17.5213090
 
5.0%
5198554
 
4.7%
21.5193607
 
4.6%
17.6170888
 
4.0%
16.4155741
 
3.7%
18145508
 
3.4%
16.8122287
 
2.9%
10.2117760
 
2.8%
Other values (3113)2438228
57.3%
ValueCountFrequency (%)
15
< 0.1%
1.031
 
< 0.1%
1.1111111118
< 0.1%
1.131
 
< 0.1%
1.21
 
< 0.1%
1.211
 
< 0.1%
1.254
< 0.1%
1.351
 
< 0.1%
1.45
< 0.1%
1.4021164021
 
< 0.1%
ValueCountFrequency (%)
28.289
< 0.1%
28.1610
 
< 0.1%
28.142
 
< 0.1%
28.131
 
< 0.1%
28.127
 
< 0.1%
28.111
 
< 0.1%
28.0811
 
< 0.1%
28.073
 
< 0.1%
28.066666671
 
< 0.1%
28.052
 
< 0.1%

total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52949
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.1939017
Minimum0.3
Maximum611.226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.9 MiB
2021-10-17T18:00:55.526806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile2.1
Q113.35
median41.6
Q3135.45
95-th percentile397.6
Maximum611.226
Range610.926
Interquartile range (IQR)122.1

Descriptive statistics

Standard deviation127.3468747
Coefficient of variation (CV)1.283817579
Kurtosis2.602744921
Mean99.1939017
Median Absolute Deviation (MAD)35.78
Skewness1.779073244
Sum422004484.6
Variance16217.2265
MonotonicityNot monotonic
2021-10-17T18:00:55.641807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9466014
 
1.6%
9.67550899
 
1.2%
3.8840662
 
1.0%
2.0437575
 
0.9%
1.2536944
 
0.9%
17.635283
 
0.8%
1635064
 
0.8%
16.434516
 
0.8%
17.531718
 
0.7%
1830714
 
0.7%
Other values (52939)3854950
90.6%
ValueCountFrequency (%)
0.31
 
< 0.1%
0.381
 
< 0.1%
0.53
< 0.1%
0.551
 
< 0.1%
0.62
< 0.1%
0.621
 
< 0.1%
0.633
< 0.1%
0.651
 
< 0.1%
0.751
 
< 0.1%
0.991
 
< 0.1%
ValueCountFrequency (%)
611.2261
 
< 0.1%
611.1361
 
< 0.1%
611.125
< 0.1%
611.17
 
< 0.1%
611.0557
 
< 0.1%
611.03251
 
< 0.1%
611.011
 
< 0.1%
610.984
 
< 0.1%
610.924
 
< 0.1%
610.831
 
< 0.1%

Interactions

2021-10-17T18:00:18.357809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:32.262283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:41.574286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:51.419284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:00.527805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:09.153805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:19.777807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:33.818298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:43.123286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:52.945282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:02.100818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:10.667822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:21.247809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:35.518282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:44.801290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:54.504283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:03.560804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:12.505806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:22.815806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:36.995283image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:46.468286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:55.944282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:04.952804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:14.025804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:24.424805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:38.524307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:48.223285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:57.471807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:06.346804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:15.404804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:25.884805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:40.090282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:49.931286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T17:59:59.069809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:07.745817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-17T18:00:16.884817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-10-17T18:00:55.754803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-17T18:00:56.112810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-17T18:00:56.447804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-17T18:00:56.782809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-17T18:00:57.133809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-17T18:00:30.614806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-17T18:00:35.560806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

iddatestore_idformatstore_namestructuredivisiondeposit/distributorstatemunicipality/citysuppliergradeskusku_descriptionsku_categorysku_subcategoryunitsunit_pricetotal
002016-01-012934GroceryOld Indigo BigComFORANEOCENTROVitena VocalsThrilvallorPurple Creekproveedor_2IV4533931Cups Mango PeachSuper-flavourCereal box0.4521.59.675
1152016-01-013098AllInOneDead Burgundy ShopDISTRIBUIDORESCENTROMovitalf EmpireAlloralfLong Chocolate Barrierproveedor_2IV4533931Cups Mango PeachSuper-flavourCereal box15.7521.5338.625
2192016-01-012026AllInOneKicking Orange HairDresserFORANEOCENTRO-SURGorngusco ToughworksLorgulgusHorrible Purple Treeproveedor_2IV4533931Cups Mango PeachSuper-flavourCereal box2.2521.548.375
3322016-01-012343LeftoriumFunny Maroon LunchHallFORANEOCENTROElortho AerospaceAlloralfFunny Magenta Treeproveedor_2IV4533931Cups Mango PeachSuper-flavourCereal box0.9021.519.350
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61562016-01-012024AllInOneAlive Beige GroceryFORANEOSURESTEEioval AerospaceCoalmarRare Orange Applepieproveedor_2VI4533931Cups Mango PeachSuper-flavourCereal box0.4521.59.675
71602016-01-013820GipsyTradeLittle Orchid BankMETROMETRO-SURIoalfio Ifonforge IndustriesMagornmarEnlightened Dark Empireproveedor_2V4533931Cups Mango PeachSuper-flavourCereal box0.9021.519.350
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92062016-01-012906GroceryLittle Pink LunchHallFORANEOCENTROElortho AerospaceAlloralfFunny Magenta Treeproveedor_2IV4533931Cups Mango PeachSuper-flavourCereal box1.3521.529.025

Last rows

iddatestore_idformatstore_namestructuredivisiondeposit/distributorstatemunicipality/citysuppliergradeskusku_descriptionsku_categorysku_subcategoryunitsunit_pricetotal
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425433154893982017-06-04119ShopDirty Lime SuperMarketFORANEOOCCIDENTEColorvit Ifonforge IndustriesNabargusOld Sea Green Soilproveedor_1III4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars0.7527.220.4
425433254894512017-06-04117ShopImpressive Indigo GroceryMETROMETRO-NORTEEmolor VocalsAlloralfHorrible Chartreuse Mudproveedor_1IV4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars3.7527.2102.0
425433354894652017-06-04254ShopLegendary Crimson TallMarketFORANEOCENTRO-SURGorngusco ToughworksLorgulgusHorrible Purple Treeproveedor_1IV4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars0.2527.26.8
425433454894992017-06-04121ShopMassive Purple HighShopMETROMETRO-SURThrilthoal EmpireMagornmarFunny Lemon Starproveedor_1V4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars5.5027.2149.6
425433554895082017-06-04251ShopDirty Sky Blue BigCenterFORANEOBAJÍOLormarmad Corp.EemarLegendary Lavender Keyproveedor_1II4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars2.2527.261.2
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425433754895532017-06-04120ShopDead Sky Blue GroceryFORANEOSURESTEEioval AerospaceCoalmarRare Orange Applepieproveedor_1VI4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars0.7527.220.4
425433854895712017-06-049ShopAlive Purple BakerDISTRIBUIDORESSURESTEArbarrus GmbhCoalmarKicking Crimson Keyproveedor_1VI4829827Golden Goals Chocolate Choc ChipSugar-freeCereal bars1.0027.227.2